Data Acquisition Method and System for an Internet of Things-Based Intelligent Welding Platform

Abstract

The present invention relates to the technical field of welding data processing, and particularly to a method and system for collecting data of an intelligent welding platform based on the Internet of Things. The method includes the steps of: performing curve fitting on the observation range of current data points, and obtaining the true index of the current data point through the fitting error of the current data point and the fitting errors of other current data points in the observation range except this current data point; obtaining the authenticity of the current data point through the Pearson correlation coefficient between the observation range of the current data point and the observation range of the corresponding voltage data point and the true index of the current data point; in the HANTS algorithm, obtaining the corrected difference through the product of the authenticity of the current data point and the difference between the current data point before and after denoising, and realizing data denoising based on the corrected difference, effectively improving the accuracy of welding data collection.

Description

Technical Field

[0001] The present invention relates to the technical field of welding data processing, and in particular to a method and system for collecting data of an intelligent welding platform based on the Internet of Things. Background Art

[0002] The intelligent welding platform is an important part of intelligent manufacturing in the industrial era, integrating a number of cutting-edge technologies, capable of quickly switching different welding tasks, meeting the production needs of small batches and multiple varieties, and improving welding quality and production efficiency. By installing various sensors on the welding machine to obtain various parameters during the welding process, remote monitoring and management of the welding process can be achieved.

[0003] In the prior art, the research on the application of welding data mostly focuses on the evaluation of welding effects. For example, the patent application document with the publication number CN115186423A discloses a method for evaluating the welding ability of a narrow lap resistance welder. This method collects strip steel and welding process parameters; calculates the nugget height and the total heat required for welding; calculates the total resistance at the weld, the effective heat generated at the weld, and the objective function value; determines whether the objective function value meets the conditions. If not, the evaluation result is that the welding requirements are not met. If so, it calculates the inner stress and outer stress of the crossbeam and column of the resistance welder frame, and then determines whether the inner stress and outer stress meet the conditions. If so, the evaluation result is that the welding requirements are met. If not, the welding parameters exceed the capabilities of the resistance welder.

[0004] The above prior art can improve production efficiency by analyzing welding process parameters to evaluate welding effects. However, there may be interference from noise data during the process of collecting welding data and transmitting data through the Internet of Things, resulting in low accuracy, and thus the accuracy of the welding evaluation effect obtained based on this is also poor.

[0005] Based on this, how to effectively improve the accuracy of welding data collection is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] In order to solve the technical problem of how to effectively improve the accuracy of welding data collection, the present invention provides a method and system for collecting data of an intelligent welding platform based on the Internet of Things.

[0007] In the first aspect, the present invention provides a method for collecting data of an intelligent welding platform based on the Internet of Things, adopting the following technical solution:

[0008] The method for collecting data of an intelligent welding platform based on the Internet of Things includes the steps:

[0009] Preset the observation ranges of current data points and voltage data points in the current data time series and voltage data time series during the preset welding process; perform curve fitting on the observation range of the current data points, obtain the ratio of the fitting error of the current data points to the average fitting error of other current data points except this current data point in the observation range, take the negative of the ratio as the exponent of the exponential function with base e to obtain the true index of this current data point; obtain the product of the Pearson correlation coefficient between the observation range of the current data point and the corresponding observation range of the voltage data point and the true index of this current data point, take the negative of the product as the exponent of the exponential function with base e to obtain the authenticity of this current data point; in the HANTS algorithm, obtain the corrected difference through the product of the authenticity of the current data point and the difference before and after denoising of this current data point, and implement data denoising based on the corrected difference.

[0010] The present invention takes into account that there may be noise interference during the current data acquisition process. Therefore, the HANTS algorithm is used to denoise the prominent current data points in the current data time series, which can effectively reduce the impact of noise data on subsequent data processing. During this process, the present invention considers that the normally changing current data points are similar to the noise data. Directly screening the noise data based on the prominence of the current data points may identify the normally changing current data as noise data. Based on this, the present invention further verifies the authenticity of the current data points by obtaining the correlation degree between the current data and the voltage data, so as to accurately identify the noise data in the current data time series and effectively improve the accuracy of welding data acquisition.

[0011] According to the data acquisition method of the intelligent welding platform based on the Internet of Things provided by the present invention, before presetting the observation ranges of current data points and voltage data points in the current data time series and voltage data time series during the welding process, it further includes: collecting the current data and voltage data during the welding process, and obtaining the current data time series and voltage data time series after preprocessing.

[0012] The present invention takes into account that the originally collected current analog data is not conducive to subsequent data processing. Therefore, the original current data is adjusted through preprocessing to facilitate subsequent data processing.

[0013] According to the data acquisition method of the intelligent welding platform based on the Internet of Things provided by the present invention, the method for obtaining the observation ranges of current data points and voltage data points includes: presetting the length of the observation range , centered on the current data point or voltage data point, and obtaining current data points or voltage data points equally on both sides to obtain the corresponding observation range.

[0014] Considering that the normal data changes gently over a period of time while the noise data points are prominent, the present invention constructs an observation range by obtaining other current data points equidistantly on both sides of the current data point, and can accurately obtain the data mutation degree of the current data point in the local range based on this.

[0015] According to the data acquisition method of the intelligent welding platform based on the Internet of Things provided by the present invention, the curve fitting of the observation range of the current data point includes: taking the time sequence number of each current data point in the observation range as the abscissa and the current value corresponding to the current data point as the ordinate for least squares fitting to obtain the fitting value of each current data point in the observation range.

[0016] According to the data acquisition method of the intelligent welding platform based on the Internet of Things provided by the present invention, the acquisition method of the difference before and after denoising of the current data point includes: decomposing the current data time sequence into multiple harmonic components, removing the high-frequency noise, and then recombining to obtain the target current data time sequence; determining the difference in data between the current data point in the current data time sequence and the target current data time sequence.

[0017] The present invention denoises the prominent current data points in the current data time sequence through the HANTS algorithm, can better retain the details of the current data itself while denoising the data, and interpolates the missing data while denoising, effectively ensuring the integrity of the current data.

[0018] According to the data acquisition method of the intelligent welding platform based on the Internet of Things provided by the present invention, the realization of data denoising based on the corrected difference includes: presetting a noise threshold; removing the current data points with the corrected difference greater than the noise threshold as noise points to realize data denoising.

[0019] According to the data acquisition method of the intelligent welding platform based on the Internet of Things provided by the present invention, after realizing data denoising, it further includes: performing anomaly monitoring on the current data time sequence during the welding process.

[0020] The present invention considers that the current data generated during the welding process can reflect the welding state. If the current data is abnormal, it indicates that there may be an abnormality during the welding process. Therefore, the present invention also performs anomaly monitoring on the current data time sequence during the welding process to reduce potential safety hazards.

[0021] In a second aspect, the present invention provides an intelligent welding platform data acquisition system based on the Internet of Things, adopting the following technical solutions:

[0022] The data acquisition system of the intelligent welding platform based on the Internet of Things includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned data acquisition method of the intelligent welding platform based on the Internet of Things is realized.

[0023] By adopting the above technical solution, the above-mentioned data acquisition method of the intelligent welding platform based on the Internet of Things is generated into a computer program and stored in the memory, so as to be loaded and executed by the processor, and thus a terminal device is made according to the memory and the processor, which is convenient to use.

[0024] The present invention has the following technical effects:

[0025] Based on the above technical solution, in the process of realizing the data acquisition of the intelligent welding platform, the present invention denoises the prominent current data points in the current data time series through the HANTS algorithm, which can effectively reduce the influence of noise data on subsequent data processing. In this process, the present invention further verifies the authenticity of the current data points by obtaining the correlation degree between the current data and the voltage data, so as to accurately identify the noise data in the current data time series, reduce the influence of the current data that is relatively similar to the noise data on the denoising effect, and effectively improve the accuracy of welding data acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0027] Figure 1 It is a flowchart showing a data acquisition method of an intelligent welding platform based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0029] It should be understood that when terms such as "first" and "second" are used in the claims, specification and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "comprising" and "including" used in the specification and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0030] It should be noted that the intelligent welding platform is an important part of intelligent manufacturing in the industrial era. It can quickly switch different welding tasks, meet the production needs of small batches and multiple varieties, and improve welding quality and production efficiency. By installing various sensors on the welding machine to obtain various parameters during the welding process, remote monitoring and management of the welding process can be achieved.

[0031] Arc welding is one of the commonly used welding methods in industrial production. It uses the heat generated by arc discharge to melt the electrode and the workpiece with each other and form a weld after condensation, so as to obtain a firm joint. However, there may be interference from noise data during the process of collecting welding data and transmitting data through the Internet of Things, resulting in low accuracy of the collected welding data.

[0032] Based on this, the embodiment of the present invention discloses a method for collecting data of an intelligent welding platform based on the Internet of Things. By denoising the collected electric welding data, the accuracy of the obtained electric welding data can be effectively improved. For details, refer to Figure 1 as shown in Figure 1 which is a schematic flowchart of a method for collecting data of an intelligent welding platform based on the Internet of Things provided by an embodiment of the present invention. The method specifically includes the following steps.

[0033] S1: Preset the observation range of the current data point in the current data time series sequence during the welding process.

[0034] Exemplarily, in the embodiment of the present invention, before presetting the observation range of the current data point in the current data time series sequence during the welding process, it further includes: collecting the current data during the welding process and obtaining the current data time series sequence after preprocessing.

[0035] Among them, the preprocessing can be data format conversion, etc., which can be specifically set according to actual needs, and the embodiment of the present invention does not limit it too much here.

[0036] Specifically, after setting the acquisition frequency for the current sensor, the current data is collected. Each acquired current value is used as a current data point, and the current data points are arranged according to the acquisition time sequence to obtain the current data time series sequence.

[0037] Among them, the acquisition frequency table can be set to 10Hz, and can be specifically set according to actual needs.

[0038] It should be noted that the Harmonic Analysis of Time Series (HANTS) is an algorithm for time series analysis and interpolation. By comparing the original time series with the fitted time series, this algorithm regards the points that deviate significantly from the fitted curve as noise points, and estimates the missing data points using the known harmonic components, so as to achieve data denoising while ensuring the integrity and continuity of the data, and retaining the detailed information of the original data. Therefore, the HANTS algorithm can accurately denoise the current data time series.

[0039] However, the current data usually changes with the change of the parameters of the items to be welded. Therefore, if the noise data points in the current data time series are directly obtained based on the difference between the original current data time series and the fitted current data time series, the normal changing current data points may be identified as noise data, affecting the accuracy of denoising.

[0040] Based on this, the embodiment of the present invention obtains the degree of damage to the local range based on the data performance of the current data point in the local range, and obtains the possibility that it is noise data. The more prominent the current data point is, the higher the possibility that it is noise data. The difference between the original current data time series and the fitted current data time series is corrected by the possibility that the current data point is noise data.

[0041] Exemplarily, in the embodiment of the present invention, the method for obtaining the observation range of the current data point specifically includes the following two possible implementation manners:

[0042] In one possible implementation manner, the length of the observation range can be preset , centered on the current data point, and an equal number of current data points are obtained on both sides to obtain the corresponding observation range.

[0043] Among them, the length of the observation range can be preset to 21, and can be specifically set according to actual needs.

[0044] It can be understood that for some current data points, the number of data points on one side may not be sufficient to construct the observation range. For such current data points, the remaining number of current data points can be supplemented on the other side.

[0045] In this way, the embodiment of the present invention obtains the current data on both sides of the current data point as the observation range of the current data point, which can better analyze the data changes on both sides of the current data point, so as to accurately obtain its data mutation situation relative to the data in the observation range.

[0046] In another possible implementation, the length of the observation range can be preset , with the current data point as the end of its observation range, and obtain current data points in the historical data to obtain the corresponding observation range.

[0047] In this way, in the embodiment of the present invention, by obtaining historical data as the observation range of the current data point, the historical data can better reflect the regular change laws and trends of the current data. Based on this, the outstanding situation of the current data point compared with the surrounding historical data can be accurately obtained.

[0048] After obtaining the observation range of the current data point based on any of the above possible implementation manners, the possibility that it is noise data can be obtained based on the outstanding situation of the current data point compared with the observation range, that is, continue to execute the following steps.

[0049] S2: Perform curve fitting on the observation range of the current data point, obtain the ratio of the fitting error of the current data point to the mean value of the fitting errors of other current data points in the observation range except this current data point, and use the negative of the ratio as the exponent of the exponential function with e as the base to obtain the true index of this current data point.

[0050] It should be noted that the fitting error during the curve fitting of the observation range can characterize the destructiveness of the current data point to the data change in its observation range. The larger the fitting error, the greater the destructiveness of the current data point to the data change in its observation range and the greater the degree of mutation.

[0051] Exemplarily, in the embodiment of the present invention, performing curve fitting on the observation range of the current data point includes: using the time sequence number of each current data point in the observation range as the abscissa and the current value corresponding to the current data point as the ordinate to perform least squares fitting to obtain the fitting values of each current data point in the observation range.

[0052] Exemplarily, to determine the true index of the current data point, the following relational expression can be specifically referred to:

[0053] ;

[0054] In the formula, represents the true index of the i-th current data point, represents the number of current data points in the observation range of the i-th current data point, represents the current value of the i-th current data point, represents the fitting value of the i-th current data point, represents the current value of the j-th current data point in the observation range of the i-th current data point, Denotes the fitted value of the j-th current data point in the observation range of the i-th current data point, excluding the i-th current data point itself. Denotes the exponential function with base e.

[0055] In the above formula, Denotes the fitting error of the i-th current data point. The larger this value, the greater the prominence of the current value of the i-th current data point relative to the entire observation range, and the lower the probability that this current data point is the real data generated by the current data itself, and the lower the corresponding real index.

[0056] Denotes the mean value of the fitting errors of other current data points in the observation range of the i-th current data point, excluding this current data point. The larger the mean value of the fitting errors, the greater the fitting errors of other current data points themselves, and the lower the credibility indicating destructiveness of the fitting error of the i-th current data point, and the lower the corresponding real index of the i-th current data point.

[0057] After obtaining the real index of each current data point based on the above formula, continue to execute the following steps.

[0058] S3: Obtain the observation range of the voltage data point corresponding to the current data point in the voltage data time series sequence to which it belongs, and correct the real index of this current data point according to the correlation degree between the observation range of the current data point and the observation range of the corresponding voltage data point, so as to obtain the authenticity of this current data point.

[0059] Among them, the method for obtaining the observation range of the voltage data point is similar to the method for obtaining the observation range of the above current data point, and the embodiments of the present invention will not elaborate too much here.

[0060] It should be noted that the current data usually changes with factors such as the parameters of the item to be welded or the welding speed. If noise data is obtained directly based on the real index of the current data point obtained in the above steps, it may identify the current data points with normal changes as noise data, reducing the real index. During the arc welding process, the increase in the welding current will cause an increase in the resistance heat of the arc, thereby increasing the arc temperature and also increasing the diameter of the arc. In order to maintain the stability of the arc and a good molten pool shape, the welding voltage will increase to adapt to such changes, and there is a good positive correlation between the voltage data and the current data.

[0061] Based on this, the embodiments of the present invention can correct the real index of the current data point obtained in the above steps by obtaining the correlation degree between the voltage data and the current data, so as to accurately obtain the authenticity of the current data point.

[0062] Exemplarily, in the embodiments of the present invention, before obtaining the observation range of the voltage data point corresponding to the current data point in the voltage data time series, it further includes: collecting the voltage data during the welding process and obtaining the voltage data time series after preprocessing.

[0063] Among them, the preprocessing method can be specifically set according to actual needs, and the embodiments of the present invention do not limit it too much here.

[0064] Specifically, after setting the acquisition frequency for the voltage sensor, the voltage data is collected, and each acquired voltage value is used as a voltage data point. The voltage data points are arranged according to the acquisition time sequence to obtain the voltage data time series. The voltage data points in the obtained voltage data time series correspond one by one to the current data points in the current data time series, that is, at the same acquisition moment, a voltage value and a current value can be collected simultaneously.

[0065] Exemplarily, in the embodiments of the present invention, the true index of the current data point is corrected through the correlation degree between the observation range of the current data point and the observation range of the corresponding voltage data point to obtain the authenticity of the current data point, including: obtaining the product of the Pearson correlation coefficient between the observation range of the current data point and the observation range of the corresponding voltage data point and the true index of the current data point, and taking the negative number of the product as the exponent of the exponential function with e as the base to obtain the authenticity of the current data point.

[0066] Exemplarily, to determine the authenticity of the current data point, the following relational expression can be specifically referred to:

[0067] ;

[0068] In the formula, represents the authenticity of the i-th current data point, represents the true index of the i-th current data point, represents the Pearson correlation coefficient between the observation range of the i-th current data point and the observation range of the corresponding voltage data point, represents the exponential function with e as the base.

[0069] After correcting the true index of the current data point based on the correlation degree between each current data point and the corresponding voltage data point obtained in the above steps, the authenticity correction difference of each current data point can be based on, that is, the following steps are continued.

[0070] S4: In the HANTS algorithm, the corrected difference is obtained through the product of the authenticity of the current data point and the difference between the current data point before and after denoising, and data denoising is realized based on the corrected difference.

[0071] It should be noted that the HANTS algorithm compares the difference between the original time series and the fitted time series, and obtains the noise points that deviate significantly from the fitted curve through the difference. However, this processing method will identify the normally changing current data as noise data. Therefore, in the embodiments of the present invention, the authenticity of each current data point is obtained based on the above steps. The higher the authenticity, the higher the possibility that the current value of the current data point is generated by normal changes.

[0072] Based on this, in the embodiments of the present invention, the difference is corrected by the authenticity of each current data point, which can effectively improve the accuracy of noise data recognition.

[0073] Exemplarily, in the embodiments of the present invention, the method for obtaining the difference between the current data points before and after denoising includes: decomposing the current data time series into multiple harmonic components, removing the high-frequency noise, and then recombining to obtain the target current data time series; determining the difference between the data of the current data point in the current data time series and the target current data time series.

[0074] Among them, the current data time series can be decomposed into multiple harmonic components through Fourier transform.

[0075] It can be understood that at the same acquisition moment, there is a corresponding current value for the current data point in both the current data time series and the target current data time series, and the difference between the current values is determined.

[0076] Exemplarily, in the embodiments of the present invention, realizing data denoising based on the corrected difference includes: presetting a noise threshold; removing the current data points with the corrected difference greater than the noise threshold as noise points to achieve data denoising.

[0077] Among them, the noise threshold can be set to 0.2, and the noise threshold can be specifically set according to actual needs. The embodiments of the present invention do not limit this too much here.

[0078] Specifically, after realizing data denoising, the low-frequency harmonic components reflecting the main characteristics of the target current data time series can also be identified and selected, and the low-frequency harmonic components are processed by the least squares method to obtain a harmonic model. The harmonic model can be used to capture the periodic changes and main trends in the target current data time series to obtain a current prediction value; the positions of the noise points are obtained, and the positions of the noise points are interpolated through the current prediction value. The data denoising and interpolation steps are repeated until the difference between all current data points in the current data time series is not greater than the noise threshold.

[0079] After denoising the current data time series based on the above steps, abnormal monitoring can also be performed on the current data time series to reduce potential safety hazards.

[0080] Exemplarily, in the embodiment of the present invention, after data denoising is achieved, it further includes: performing anomaly monitoring on the time series of current data during the welding process.

[0081] Among them, the specific steps of performing anomaly monitoring on the time series of current data can be implemented by existing technologies, and the embodiments of the present invention will not elaborate herein.

[0082] It can be seen that in the embodiment of the present invention, when implementing data acquisition of the intelligent welding platform based on the Internet of Things, the observation ranges of current data points and voltage data points during the welding process in the current data time series and voltage data time series can be preset; curve fitting is performed on the observation range of the current data point, and the ratio of the fitting error of the current data point to the average value of the fitting errors of other current data points in the observation range except this current data point is obtained. The negative of the ratio is used as the exponent of the exponential function with base e to obtain the true index of this current data point; the product of the Pearson correlation coefficient between the observation range of the current data point and the corresponding observation range of the voltage data point and the true index of this current data point is obtained, and the negative of the product is used as the exponent of the exponential function with base e to obtain the authenticity of this current data point; in the HANTS algorithm, the corrected difference is obtained through the product of the authenticity of the current data point and the difference before and after denoising of this current data point, and data denoising is achieved based on the corrected difference.

[0083] In this way, the embodiment of the present invention can effectively reduce the influence of noise data on subsequent data processing by denoising the prominent current data points in the time series of current data through the HANTS algorithm. In this process, the embodiment of the present invention considers that the normally varying current data points are similar to the noise data. Based on this, the embodiment of the present invention further verifies the authenticity of the current data points by obtaining the correlation degree between the current data and the voltage data, so as to accurately identify the noise data in the time series of current data and effectively improve the accuracy of welding data acquisition.

[0084] The embodiment of the present invention also discloses a data acquisition system for an intelligent welding platform based on the Internet of Things, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the data acquisition method for the intelligent welding platform based on the Internet of Things provided by the present invention is implemented.

[0085] The above system further includes other components well-known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be elaborated herein.

[0086] In the present invention, the foregoing memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as, resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application program, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0087] Although this specification has shown and described several embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, modifications, and alternative forms will occur to those skilled in the art without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0088] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered by the protection scope of the present invention.

Claims

1. A method for collecting data of an intelligent welding platform based on the Internet of Things, characterized in that Including: The observation ranges of the current data points and voltage data points during the preset welding process in the current data time series sequence and voltage data time series sequence where they are located; Perform curve fitting on the observation range of the current data points, obtain the ratio of the fitting error of the current data points to the average value of the fitting errors of other current data points except this current data point in the observation range, and take the negative of the ratio as the exponent of the exponential function with base e to obtain the true index of this current data point; Obtain the product of the Pearson correlation coefficient between the observation range of the current data points and the observation range of the corresponding voltage data points and the true index of this current data point, and take the negative of the product as the exponent of the exponential function with base e to obtain the authenticity of this current data point; In the HANTS algorithm, obtain the corrected difference through the product of the authenticity of the current data point and the difference between the current data point before and after denoising, and implement data denoising based on the corrected difference.

2. The data acquisition method of the intelligent welding platform based on the Internet of Things according to claim 1, wherein Before the observation ranges of the current data points and voltage data points during the preset welding process in the current data time series sequence and voltage data time series sequence where they are located, it also includes: Collect the current data and voltage data during the welding process, and obtain the current data time series sequence and voltage data time series sequence after preprocessing.

3. The data acquisition method of the intelligent welding platform based on the Internet of Things according to claim 1, wherein The method for obtaining the observation ranges of the current data points and voltage data points includes: Length of the preset observation range , centered on the current data point or voltage data point, obtain current data points or voltage data points on both sides equally to obtain the corresponding observation range.

4. The data acquisition method of the intelligent welding platform based on the Internet of Things according to claim 1, wherein The curve fitting of the observation range of the current data points includes: Perform least squares fitting with the time series number of each current data point in the observation range as the abscissa and the current value corresponding to the current data point as the ordinate to obtain the fitting values of each current data point in the observation range.

5. The data acquisition method of the intelligent welding platform based on the Internet of Things according to claim 1, characterized in that The method for obtaining the difference between the current data point before and after denoising includes: Decompose the current data time series sequence into multiple harmonic components, remove the high-frequency noise, and then recombine to obtain the target current data time series sequence; determine the difference between the data of the current data point in the current data time series sequence and the target current data time series sequence.

6. The data acquisition method of the intelligent welding platform based on the Internet of Things according to claim 5, characterized in that The implementation of data denoising based on the corrected difference includes: Preset a noise threshold; remove the current data points with the corrected difference greater than the noise threshold as noise points to achieve data denoising.

7. The data acquisition method of the intelligent welding platform based on the Internet of Things according to claim 6, characterized in that, After the implementation of data denoising, it also includes: Perform anomaly monitoring on the current data time series sequence during the welding process.

8. The data acquisition system of the intelligent welding platform based on the Internet of Things is characterized in that Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, it realizes the data acquisition method of the intelligent welding platform based on the Internet of Things according to any one of claims 1-7.

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